dmlc--dgl
Hao Zhang
49 行
2.4 KiB
Plaintext
49 行
2.4 KiB
Plaintext
.. _tutorials1-index:
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Graph Neural Network and its variant
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====================================
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* **GCN** `[paper] <https://arxiv.org/abs/1609.02907>`__ `[tutorial]
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<1_gnn/1_gcn.html>`__ `[Pytorch code]
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<https://github.com/dmlc/dgl/blob/master/examples/pytorch/gcn>`__
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`[MXNet code]
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<https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn>`__:
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this is the vanilla GCN. The tutorial covers the basic uses of DGL APIs.
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* **GAT** `[paper] <https://arxiv.org/abs/1710.10903>`__ `[tutorial]
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<1_gnn/9_gat.html>`__ `[Pytorch code]
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<https://github.com/dmlc/dgl/blob/master/examples/pytorch/gat>`__
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`[MXNet code]
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<https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat>`__:
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the key extension of GAT w.r.t vanilla GCN is deploying multi-head attention
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among neighborhood of a node, thus greatly enhances the capacity and
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expressiveness of the model.
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* **R-GCN** `[paper] <https://arxiv.org/abs/1703.06103>`__ `[tutorial]
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<1_gnn/4_rgcn.html>`__ `[Pytorch code]
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<https://github.com/dmlc/dgl/tree/master/examples/pytorch/rgcn>`__
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`[MXNet code]
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<https://github.com/dmlc/dgl/tree/master/examples/mxnet/rgcn>`__:
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the key difference of RGNN is to allow multi-edges among two entities of a
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graph, and edges with distinct relationships are encoded differently. This
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is an interesting extension of GCN that can have a lot of applications of
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its own.
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* **LGNN** `[paper] <https://arxiv.org/abs/1705.08415>`__ `[tutorial]
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<1_gnn/6_line_graph.html>`__ `[Pytorch code]
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<https://github.com/dmlc/dgl/tree/master/examples/pytorch/line_graph>`__:
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this model focuses on community detection by inspecting graph structures. It
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uses representations of both the original graph and its line-graph
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companion. In addition to demonstrate how an algorithm can harness multiple
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graphs, our implementation shows how one can judiciously mix vanilla tensor
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operation, sparse-matrix tensor operations, along with message-passing with
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DGL.
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* **SSE** `[paper] <http://proceedings.mlr.press/v80/dai18a/dai18a.pdf>`__ `[tutorial]
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<1_gnn/8_sse_mx.html>`__ `[MXNet code]
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<https://github.com/dmlc/dgl/blob/master/examples/mxnet/sse>`__:
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the emphasize here is *giant* graph that cannot fit comfortably on one GPU
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card. SSE is an example to illustrate the co-design of both algorithm and
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system: sampling to guarantee asymptotic convergence while lowering the
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complexity, and batching across samples for maximum parallelism.
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